A novel enhanced exergy method in analyzing HVAC system using soft computing approaches: A case study on mushroom growing hall. (September 2017)

• Record Type: Journal Article
• Authors: Faizollahzadeh Ardabili, Sina; Najafi, Bahman; Ghaebi, Hadi; Shamshirband, Shahaboddin; Mostafaeipour, Ali
• Is Part Of: Journal of building engineering. Volume 13(2017)
• Page Start: 309
• Page End: 318
• Publication Date: 2017-09
• Subjects: Adaptive neuro fuzzy inference system (ANFIS) -- Energy crisis -- Exergy -- Heating -- Ventilating and air conditioning (HVAC) system -- Multi layered perceptron (MLP); Building -- Periodicals; 690.05
• DOI: 10.1016/j.jobe.2017.08.008
• Languages: English
• ISSNs: 2352-7102

Abstract

Abstract: Energy crisis concentrates attentions in the field of building energy consumption through optimization of HVAC control systems. Studying the HVAC systems and optimizing them will help to save energy. Exergy is defined as a new energy function that can maximize accessible work by the second law of thermodynamics. The present study, discusses about HVAC system that is in operation for mushroom growing hall. The Exergy destruction is calculated for HVAC and the whole system and is linked to effective parameters as independent variables. Adaptive neuro fuzzy inference system (ANFIS) and multi layered perceptron (MLP) methods are used to model the studied system. Accordingly, after training by different number of neurons in the hidden layer for MLP network and by different types of membership function for ANFIS method, 10 numbers of neurons were selected as the best number of neurons for MLP network and Gaussian type of membership function for ANFIS method. The results indicate that MLP by consumption of 11.556 kj/s more energy compared to ANFIS, imposes 1.343 × 10 −5 $/s more cost and 2.687 × 10 −4 m 3 /s more consumption of natural gas. Therefore, applying ANFIS model prevents energy, time, cost losses and more GHG emission, so it can be the best and suitable model to adopt in real system. Highlights: Optimization was performed by Response Surface Method (RSM). ANFIS with linearity of 0.9982 and RMSE of 0.0681 had the best performance in predicting dependent variables with deviation of 0.9694 kj/s. MLP method had a poor performance in this study by linearity of 0.9511 and RMSE of 0.5584 with deviation of 12.5254 kj/s.